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--- |
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language: |
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- en |
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tags: |
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- stable-diffusion |
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- text-to-image |
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license: bigscience-bloom-rail-1.0 |
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inference: false |
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--- |
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# waifu-diffusion - Diffusion for Weebs |
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waifu-diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through [Textual Inversion](https://github.com/rinongal/textual_inversion). |
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<img src=https://cdn.discordapp.com/attachments/872361510133981234/1016022078635388979/unknown.png?3867929 width=40% height=40%> |
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<sub>Prompt: touhou 1girl komeiji_koishi portrait</sub> |
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## Model Description |
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The model originally used for fine-tuning is [Stable Diffusion V1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), which is a latent image diffusion model trained on [LAION2B-en](https://huggingface.co/datasets/laion/laion2B-en). |
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The current model has been fine-tuned with a learning rate of 5.0e-5 for 1 epoch on 56k Danbooru text-image pairs which all have an aesthetic rating greater than `6.0`. |
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## Training Data & Annotative Prompting |
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The data used for Textual Inversion has come from a random sample of 56k Danbooru images, which were filtered based on [CLIP Aesthetic Scoring](https://github.com/christophschuhmann/improved-aesthetic-predictor) where only images with an aesthetic score greater than `6.0` were used. |
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Captions are Danbooru-style captions. |
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## Downstream Uses |
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This model can be used for entertainment purposes and as a generative art assistant. |
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## Example Code |
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```python |
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import torch |
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from torch import autocast |
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from diffusers import StableDiffusionPipeline |
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model_id = "hakurei/waifu-diffusion" |
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device = "cuda" |
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision='fp16') |
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pipe = pipe.to(device) |
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prompt = "touhou hakurei_reimu 1girl solo portrait" |
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with autocast("cuda"): |
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image = pipe(prompt, guidance_scale=7.5)["sample"][0] |
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image.save("reimu_hakurei.png") |
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``` |
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## Team Members and Acknowledgements |
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This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/). |
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- [Anthony Mercurio](https://github.com/harubaru) |
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- [Salt](https://github.com/sALTaccount/) |
